Combining Machine Learning

Combining machine learning (ML) techniques focuses on leveraging the strengths of different algorithms and data sources to improve model performance, interpretability, and efficiency in diverse applications. Current research emphasizes integrating ML with other methods, such as support vector machines, vision transformers, and computational fluid dynamics, to address challenges in areas like drought detection, causal inference, and complex system modeling. This interdisciplinary approach is proving valuable for enhancing the accuracy and applicability of ML across various scientific domains and practical problems, ranging from agriculture to engineering and fake news detection.